R Programming for Data Science
Category: IT & SoftwareCategory: Personal Development
Course Info
Overview
Develop a R Programming for Data Science arsenal of skills including critical strategic, managerial, and leadership abilities with our expertly developed R Programming for Data Science.
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Through an immersive online experience, Learning Paths offers market-driven courses that empower you or any working professional with the competence required for the workplace of the future. We assess future skill demands using a data-driven methodology and ensure that all of our courses satisfy this need. This R Programming for Data Science course is no exception.
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Description
This R Programming for Data Science course will teach you how to think critically and strategically about R Programming for Data Science, as well as how to build and implement strategy. You’ll also discover fundamental R Programming for Data Science ideas that will help you build the foundation you need to flourish in the workplace.This Learning Paths online R Programming for Data Science course is for working people who want to improve their abilities and advance in their careers. The R Programming for Data Science course is provided in an interactive virtual learning environment where you can study at your own comfort and convenience.
Who is this course for?
This certificate R Programming for Data Science course is curated for individuals who want to improve their hard and soft skills. The interactive, guided approach to learning and the potential to build their worldwide network online will help you thrive. Moreover, working professionals in managerial and leadership roles in a variety of industries will benefit from the emphasis on the dynamics of leadership, influence, and strategy, as well as R Programming for Data Science abilities. Those pursuing career advancement in the future will benefit from the skill to apply R Programming for Data Science-derived abilities to current and future employment.
Requirements
This Learning Paths R Programming for Data Science course will prepare you to make data-driven decisions that will give you a competitive edge. There are no formal requirements for this course. However smart gadgets and stable internet connection is required for a smooth learning journey.
Career Path
Earn a certificate of competence from the Learning Paths platform by learning all about R Programming for Data Science. This R Programming for Data Science course curriculum will benefit you at all stages of your career.
Certification
After successfully completing the R Programming for Data Science course, you will get your PDF certificate for FREE! The hardcopy certificate will cost only £11.99 with free shipping inside the UK. For delivery outside the UK an additional shipping charge will be applied.
Course Curriculum
| Unit 01: Data Science Overview | |||
| Introduction to Data Science | 00:01:00 | ||
| Data Science: Career of the Future | 00:04:00 | ||
| What is Data Science? | 00:02:00 | ||
| Data Science as a Process | 00:02:00 | ||
| Data Science Toolbox | 00:03:00 | ||
| Data Science Process Explained | 00:05:00 | ||
| What’s Next? | 00:01:00 | ||
| Unit 02: R and RStudio | |||
| Engine and coding environment | 00:03:00 | ||
| Installing R and RStudio | 00:04:00 | ||
| RStudio: A quick tour | 00:04:00 | ||
| Unit 03: Introduction to Basics | |||
| Arithmetic with R | 00:03:00 | ||
| Variable assignment | 00:04:00 | ||
| Basic data types in R | 00:03:00 | ||
| Unit 04: Vectors | |||
| Creating a vector | 00:05:00 | ||
| Naming a vector | 00:04:00 | ||
| Arithmetic calculations on vectors | 00:07:00 | ||
| Vector selection | 00:06:00 | ||
| Selection by comparison | 00:04:00 | ||
| Unit 05: Matrices | |||
| What’s a Matrix? | 00:02:00 | ||
| Analyzing Matrices | 00:03:00 | ||
| Naming a Matrix | 00:05:00 | ||
| Adding columns and rows to a matrix | 00:06:00 | ||
| Selection of matrix elements | 00:03:00 | ||
| Arithmetic with matrices | 00:07:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 06: Factors | |||
| What’s a Factor? | 00:02:00 | ||
| Categorical Variables and Factor Levels | 00:04:00 | ||
| Summarizing a Factor | 00:01:00 | ||
| Ordered Factors | 00:05:00 | ||
| Unit 07: Data Frames | |||
| What’s a Data Frame? | 00:03:00 | ||
| Creating Data Frames | 00:20:00 | ||
| Selection of Data Frame elements | 00:03:00 | ||
| Conditional selection | 00:03:00 | ||
| Sorting a Data Frame | 00:03:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 08: Lists | |||
| Why would you need lists? | 00:01:00 | ||
| Creating a List | 00:06:00 | ||
| Selecting elements from a list | 00:03:00 | ||
| Adding more data to the list | 00:02:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 09: Relational Operators | |||
| Equality | 00:03:00 | ||
| Greater and Less Than | 00:03:00 | ||
| Compare Vectors | 00:03:00 | ||
| Compare Matrices | 00:02:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 10: Logical Operators | |||
| AND, OR, NOT Operators | 00:04:00 | ||
| Logical operators with vectors and matrices | 00:04:00 | ||
| Reverse the result: (!) | 00:01:00 | ||
| Relational and Logical Operators together | 00:06:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 11: Conditional Statements | |||
| The IF statement | 00:04:00 | ||
| IF…ELSE | 00:03:00 | ||
| The ELSEIF statement | 00:05:00 | ||
| Full Exercise | 00:03:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 12: Loops | |||
| Write a While loop | 00:04:00 | ||
| Looping with more conditions | 00:04:00 | ||
| Break: stop the While Loop | 00:04:00 | ||
| What’s a For loop? | 00:02:00 | ||
| Loop over a vector | 00:02:00 | ||
| Loop over a list | 00:03:00 | ||
| Loop over a matrix | 00:04:00 | ||
| For loop with conditionals | 00:01:00 | ||
| Using Next and Break with For loop | 00:03:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 13: Functions | |||
| What is a Function? | 00:02:00 | ||
| Arguments matching | 00:03:00 | ||
| Required and Optional Arguments | 00:03:00 | ||
| Nested functions | 00:02:00 | ||
| Writing own functions | 00:03:00 | ||
| Functions with no arguments | 00:02:00 | ||
| Defining default arguments in functions | 00:04:00 | ||
| Function scoping | 00:02:00 | ||
| Control flow in functions | 00:03:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 14: R Packages | |||
| Installing R Packages | 00:01:00 | ||
| Loading R Packages | 00:04:00 | ||
| Different ways to load a package | 00:02:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 15: The Apply Family - lapply | |||
| What is lapply and when is used? | 00:04:00 | ||
| Use lapply with user-defined functions | 00:03:00 | ||
| lapply and anonymous functions | 00:01:00 | ||
| Use lapply with additional arguments | 00:04:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 16: The apply Family – sapply & vapply | |||
| What is sapply? | 00:02:00 | ||
| How to use sapply | 00:02:00 | ||
| sapply with your own function | 00:02:00 | ||
| sapply with a function returning a vector | 00:02:00 | ||
| When can’t sapply simplify? | 00:02:00 | ||
| What is vapply and why is it used? | 00:04:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 17: Useful Functions | |||
| Mathematical functions | 00:05:00 | ||
| Data Utilities | 00:08:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 18: Regular Expressions | |||
| grepl & grep | 00:04:00 | ||
| Metacharacters | 00:05:00 | ||
| sub & gsub | 00:02:00 | ||
| More metacharacters | 00:04:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 19: Dates and Times | |||
| Today and Now | 00:02:00 | ||
| Create and format dates | 00:06:00 | ||
| Create and format times | 00:03:00 | ||
| Calculations with Dates | 00:03:00 | ||
| Calculations with Times | 00:07:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 20: Getting and Cleaning Data | |||
| Get and set current directory | 00:04:00 | ||
| Get data from the web | 00:04:00 | ||
| Loading flat files | 00:03:00 | ||
| Loading Excel files | 00:05:00 | ||
| Additional Materials | 00:00:00 | ||
| Unit 21: Plotting Data in R | |||
| Base plotting system | 00:03:00 | ||
| Base plots: Histograms | 00:03:00 | ||
| Base plots: Scatterplots | 00:05:00 | ||
| Base plots: Regression Line | 00:03:00 | ||
| Base plots: Boxplot | 00:03:00 | ||
| Unit 22: Data Manipulation with dplyr | |||
| Introduction to dplyr package | 00:04:00 | ||
| Using the pipe operator (%>%) | 00:02:00 | ||
| Columns component: select() | 00:05:00 | ||
| Columns component: rename() and rename_with() | 00:02:00 | ||
| Columns component: mutate() | 00:02:00 | ||
| Columns component: relocate() | 00:02:00 | ||
| Rows component: filter() | 00:01:00 | ||
| Rows component: slice() | 00:04:00 | ||
| Rows component: arrange() | 00:01:00 | ||
| Rows component: rowwise() | 00:02:00 | ||
| Grouping of rows: summarise() | 00:03:00 | ||
| Grouping of rows: across() | 00:02:00 | ||
| COVID-19 Analysis Task | 00:08:00 | ||
| Additional Materials | 00:00:00 | ||
| Assignment | |||
| Assignment – R Programming for Data Science | 00:00:00 | ||
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